LLM aided semi-supervision for Extractive Dialog Summarization.
CoRR(2023)
摘要
Generating high-quality summaries for chat dialogs often requires large
labeled datasets. We propose a method to efficiently use unlabeled data for
extractive summarization of customer-agent dialogs. In our method, we frame
summarization as a question-answering problem and use state-of-the-art large
language models (LLMs) to generate pseudo-labels for a dialog. We then use
these pseudo-labels to fine-tune a chat summarization model, effectively
transferring knowledge from the large LLM into a smaller specialized model. We
demonstrate our method on the \tweetsumm dataset, and show that using 10\% of
the original labelled data set we can achieve 65.9/57.0/61.0 ROUGE-1/-2/-L,
whereas the current state-of-the-art trained on the entire training data set
obtains 65.16/55.81/64.37 ROUGE-1/-2/-L. In other words, in the worst case
(i.e., ROUGE-L) we still effectively retain 94.7% of the performance while
using only 10% of the data.
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